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Tastes Great! Less Filling! High Performance and Accurate Training Data Collection for Self-Driving Database Management Systems
Summary: TScout collects training data for self-driving DBMSs by annotating source with hooks and generating kernel-level BPF probes. It aggregates workload, config, internal state, and hardware metrics in a PostgreSQL-compatible DBMS, with ~7% overhead, yielding better ML behavior models for OLTP/OLAP.
(summarized by gpt-5-nano on Feb 09 2026)
- Paper ID
- 6297
- Venue
- SIGMOD
- Year
- 2022
- Pagerank
- 4.5905454e-05
- Overall Rank
- 8,082 | 43.78%
- DOI
-
10.1145/3514221.3517845
Incoming Non-self Citations Over Time
Incoming Citations (Sorted by Pagerank)
Showing 10 of 10 citing papers.
| Rank |
Citing Paper |
Year |
Venue |
Pagerank |
| 6,885 |
PilotScope: Steering Databases with Machine Learning Drivers |
2024 |
VLDB |
4.895386e-05 |
| 8,009 |
CAMAL: Optimizing LSM-trees via Active Learning |
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| 8,020 |
The Holon Approach for Simultaneously Tuning Multiple Components in a Self-Driving Database Management System with Machine Learning via Synthesized Proto-Actions |
2024 |
VLDB |
4.6040862e-05 |
| 8,671 |
Algorithmic Complexity Attacks on Dynamic Learned Indexes |
2024 |
VLDB |
4.4714076e-05 |
| 8,839 |
BPF-DB: A Kernel-Embedded Transactional Database Management System For eBPF Applications |
2025 |
SIGMOD |
4.4388652e-05 |
| 9,006 |
Hit the Gym: Accelerating Query Execution to Efficiently Bootstrap Behavior Models for Self-Driving Database Management Systems |
2024 |
VLDB |
4.4101482e-05 |
| 9,185 |
Practical DB-OS Co-Design with Privileged Kernel Bypass |
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SIGMOD |
4.3792034e-05 |
| 9,467 |
Database Gyms |
2023 |
CIDR |
4.3346412e-05 |
| 9,806 |
The Image Calculator: 10x Faster Image-AI Inference by Replacing JPEG with Self-designing Storage Format |
2024 |
SIGMOD |
4.2805224e-05 |
| 9,956 |
SCompression: Enhancing Database Knob Tuning Efficiency Through Slice-Based OLTP Workload Compression |
2025 |
VLDB |
4.2373024e-05 |
Outgoing Citations (Sorted by Pagerank)
Showing 20 of 20 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank |
Cited Paper |
Year |
Venue |
Pagerank |
| 60 |
Efficiently Compiling Efficient Query Plans for Modern Hardware |
2011 |
VLDB |
0.00064439773 |
| 183 |
Automatic Database Management System Tuning Through Large-scale Machine Learning |
2017 |
SIGMOD |
0.00036721403 |
| 340 |
OLTP-Bench: An Extensible Testbed for Benchmarking Relational Databases |
2014 |
VLDB |
0.00026841628 |
| 349 |
Serializable Isolation for Snapshot Databases |
2008 |
SIGMOD |
0.00026440605 |
| 359 |
Self-Driving Database Management Systems |
2017 |
CIDR |
0.0002592783 |
| 419 |
Fast Serializable Multi-Version Concurrency Control for Main-Memory Database Systems |
2015 |
SIGMOD |
0.00023720338 |
| 424 |
Tuning Database Configuration Parameters with iTuned |
2009 |
VLDB |
0.00023616398 |
| 514 |
An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning |
2019 |
SIGMOD |
0.0002124895 |
| 716 |
Query-based Workload Forecasting for Self-Driving Database Management Systems |
2018 |
SIGMOD |
0.00017723171 |
| 782 |
QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning |
2019 |
VLDB |
0.00016729063 |
| 801 |
SageDB: A Learned Database System |
2019 |
CIDR |
0.00016505496 |
| 884 |
Plan-Structured Deep Neural Network Models for Query Performance Prediction |
2019 |
VLDB |
0.00015654004 |
| 2,047 |
Automatically Indexing Millions of Databases in Microsoft Azure SQL Database |
2019 |
SIGMOD |
9.6920209e-05 |
| 2,230 |
Performance and Resource Modeling in Highly-Concurrent OLTP Workloads |
2013 |
SIGMOD |
9.2322426e-05 |
| 3,580 |
Query Performance Prediction for Concurrent Queries using Graph Embedding |
2020 |
VLDB |
6.9500996e-05 |
| 3,725 |
Estimating Cardinalities with Deep Sketches |
2019 |
SIGMOD |
6.8170734e-05 |
| 4,152 |
openGauss: An Autonomous Database System |
2021 |
VLDB |
6.4060406e-05 |
| 4,590 |
MB2: Decomposed Behavior Modeling for Self-Driving Database Management Systems |
2021 |
SIGMOD |
6.0620053e-05 |
| 6,666 |
Mainlining Databases: Supporting Fast Transactional Workloads on Universal Columnar Data File Formats |
2021 |
VLDB |
4.9691571e-05 |
| 8,180 |
Demonstrating UDO: A Unified Approach for Optimizing Transaction Code, Physical Design, and System Parameters via Reinforcement Learning |
2021 |
SIGMOD |
4.5663204e-05 |
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